Identification of galaxy clusters in cosmic microwave background maps using the Sunyaev-Zel'dovich effect

TL;DR

This paper presents a modified JADE algorithm for identifying galaxy clusters via the Sunyaev-Zel'dovich effect in CMB maps, demonstrating high accuracy in simulated noisy datasets without prior assumptions.

Contribution

The study introduces a noise-robust JADE-based method for SZ cluster detection, offering an effective alternative to existing algorithms in CMB data analysis.

Findings

Purities higher than 90% in cluster identification

Effective recovery of SZ sources from noisy data

No a priori assumptions needed for detection

Abstract

The Planck satellite was launched in 2009 by the European Space Agency to study the properties of the cosmic microwave background (CMB). An expected result of the Planck data analysis is the distinction of the various contaminants of the CMB signal. Among these contaminants is the Sunyaev-Zel'dovich (SZ) effect, which is caused by the inverse Compton scattering of CMB photons by high energy electrons in the intracluster medium of galaxy clusters. We modify a public version of the JADE (Joint Approximate Diagonalization of Eigenmatrices) algorithm, to deal with noisy data, and then use this algorithm as a tool to search for SZ clusters in two simulated datasets. The first dataset is composed of simple "homemade" simulations and the second of full sky simulations of high angular resolution, available at the LAMBDA (Legacy Archive for Microwave Background Data Analysis) website. The process of component separation can be summarized in four main steps: (1) pre-processing based on wavelet analysis, which performs an initial cleaning (denoising) of data to minimize the noise level; (2) the separation of the components by JADE; (3) the calibration of the recovered SZ map; and (4) the identification of the positions and intensities of the clusters using the SExtractor software. The results show that our JADE-based algorithm is effective in identifying the position and intensity of the SZ clusters, with the purities being higher then 90% for the extracted "catalogues". This value changes slightly according to the characteristics of noise and the number of components included in the input maps. The main highlight of our developed work is the effective recovery rate of SZ sources from noisy data, with no a priori assumptions. This powerful algorithm can be easily implemented and become an interesting complementary option to the "matched filter" algorithm widely used in SZ data analysis.